AI Agent Control and Governance
The thread began on 8 April with “From Governance Norms to Enforceable Controls: A Layered Translation Method for Runtime Guardrails in Agentic AI,” which proposed translating governance norms into controls that operate while agents run. On 19 June, “Deontic Policies for Runtime Governance of Agentic AI Systems” developed the policy side of the problem. Four days later, “Specifying AI-SDLC Processes: A Protocol Language for Human-Agent Boundaries” addressed how teams could define boundaries between people and agents during the AI software development lifecycle. “A Theory of Least Autonomy” followed on 14 July and proposed a principle for limiting how much freedom an AI system receives. On 23 July, “Graph-Based Agentic AI with LangGraph: Workflow Pathways for Long-Running Stateful Business Processes” shifted the discussion to graph-based workflows for persistent business processes. The latest signal, “Contract monitoring: governing AI via separation of powers,” appeared on 29 September and framed governance through contract monitoring and divided authority.
The papers trace a research agenda from broad norms to runtime policies, explicit boundaries, constrained autonomy, structured workflows, and monitored contracts. This remains background research supported entirely by ArXiv papers.
Assuming the claims are factual, impact is 10%. The immediate audience is researchers, agent developers, and governance teams deciding how much autonomy to grant systems and how to enforce limits during operation. The work expands the available design vocabulary. These signals provide no evidence of broad deployment or adoption.
Watch for working implementations, empirical evaluations, and adoption outside ArXiv CS.AI. Evidence that these methods can control long-running agents in operational settings would materially change the picture.
arXiv:2605.05440v1 Announce Type: new Abstract: The security discussion around agentic AI focuses heavily on prompt injection. This paper argues that multi-agent systems also
arXiv:2604.23280v1 Announce Type: new Abstract: AI agents are now running real transactions, workflows, and sub-agent chains across organizational boundaries without continuous
arXiv:2605.12963v1 Announce Type: new Abstract: As AI systems become increasingly capable, safety strategies must be evaluated not only by how much they reduce present risk, but
arXiv:2604.27292v1 Announce Type: new Abstract: Every system that performs effects has two boundaries: what it can do (expressiveness) and what governance covers (governance). In
arXiv:2604.22748v1 Announce Type: new Abstract: As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment
arXiv:2604.05080v1 Announce Type: cross Abstract: We present Nidus, a governance runtime that mechanizes the V-model for AI-assisted software delivery. In the self-hosting
arXiv:2605.00055v1 Announce Type: cross Abstract: We report a safety incident in a deployed multi-agent research system in which a primary AI agent installed 107 unauthorized
arXiv:2605.11496v1 Announce Type: new Abstract: Recent published evidence from frontier laboratories shows that contemporary AI models can recognise evaluation contexts, latently
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